DESCRIPTION : (Adapted from the Applicant's Abstract): This application for the retirement transition has generally been recognized, existing investigations of this effect have been hampered by the lack of longitudinal data containing adequate information on both health status and the financial constraints individuals face. At the same time, previous research into the effects of financial incentives on retirement have been limited by sparse data on health status. The investigators propose to use well-specified longitudinal economic models to analyze how health status and economic factors jointly affect labor force behavior as workers approach retirement age, with particular focus on the choices people make when their health declines. Data will come from the ongoing Health and Retirement Survey (HRS), which contains precisely the variables needed to model the labor force behavior of older workers. In descriptive analyses, they will describe salient labor force trajectories among workers aged 50+, and the economic circumstances of individuals with different types of trajectories; in both types of analysis, we will distinguishing between individuals in different states of health. The investigators will then estimate hazard models and dynamic programming models of labor force behavior. Finally, they will compare results across these types of econometric models. The investigators intend the proposed research to serve several important purposes. First, it should improve understanding of labor force behavior as people age, by tying together two important but hitherto largely separate lines of research: research into the effects of health and of economic factors, respectively, on retirement behavior. In doing so, it should provide timely evidence on the possible behavioral and fiscal effects of changes in such programs as Social Security, disability insurance, and Medicare. In addition, the findings should provide additional information on the quality and uses of the detailed health and economic data available in the HRS and help quantify the possible limitations of using less detailed data sets.